How 25 Years in OT Revealed Key Risks in IT-Led AI Deployments
You spent 25 years at RoviSys working in MES, historians, and other Level 3 systems before launching the Industrial AI division. How did that OT background shape your approach to AI?
When we launched the Industrial AI division in 2019, customer conversations were our main focus. We identified a clear market opportunity: some of our customers had small data science teams exploring AI, and new AI vendors were entering the OT space. But we kept hearing the same issue. These teams could develop ML models and predictive models that worked. However, after creating the models, everyone would wonder, “Who knows how to put this into operation on the plant floor?” There was a fundamental gap between the skills of data science teams and those necessary to keep a plant running 24/7. Customers told us they had success building models but struggled to move beyond the pilot stage.
So, what does it really take to operationalize an ML model on the plant floor? It’s not just about getting it up and running. You need a comprehensive approach that includes organizational change management. Consider this: you’re asking an operator who has spent over a decade learning to trust their senses to now trust an AI model. That demands significant organizational change. One reason many projects fail? Data science teams didn’t involve operators from the beginning. We know that for these projects to succeed, you must include the people who use them daily from the start. Anything seen as being forced from above is usually rejected outright.
We understood how to handle the people aspect. We recognized that processes would need adjustments, often requiring updates to the MES system and integration with the control system. We learned how to deploy technology on the plant floor that must operate 24/7. We knew all models experience drift, so a strategy for retraining and redeploying is essential. Importantly, from a technology perspective, despite many AI vendors promoting a cloud-based, closed-loop message, nearly none of our customers would accept that. Our customers still insist that any ML models deployed must be located either in the DMZ or the plant control network and remain completely disconnected from the internet. With this understanding, we decided, “Let’s get into this industry because we can make a difference with a different story.”